The coordinates of the Legionnairess Disease outbreaks will be used to gather weather data from surrounding stations. The averages of the data will be taken and outputted into a graph containing data from the last 10 years before the outbreak.

library(devtools)
library(rnoaa)
library(countyweather)
library(dplyr)
library(plyr)
library(tidyr)
library(weathermetrics)
library(ggplot2)
library(lubridate)
library(knitr)

I created a data frame including the locations of each outbreak. I found the coordinates at http://maps.cga.harvard.edu/gpf/ and crossed checked them with Google coordinates. The other data in this set are year of outbreak and the year 10 years before the outbreak, onset date, and 14 days before the onset date.

##                   id           file_id latitude longitude year_min
## 1           portugal          portugal    38.96     -8.99     2004
## 2         pittsburgh        pittsburgh    40.43    -79.98     2002
## 3             quebec            quebec    46.85    -71.34     2002
## 4     stoke-on-trent    stoke_on_trent    53.02     -2.15     2002
## 5          edinburgh         edinburgh    55.94     -3.20     2002
## 6           miyazaki          miyazaki    31.89    131.34     1992
## 7      pas-de-calais     pas_de_calais    50.51      2.37     1993
## 8           pamplona          pamplona    42.81     -1.65     1996
## 9         rapid city        rapid_city    44.06   -103.22     1995
## 10         sarpsborg         sarpsborg    59.28     11.08     1995
## 11 barrow-in-furness barrow_in_furness    54.10     -3.22     1992
## 12            murcia            murcia    37.98     -1.12     1991
## 13         melbourne         melbourne   -37.86    145.07     1990
## 14      bovenkarspel      bovenkarspel    52.70      5.24     1989
## 15            london            london    51.52     -0.10     1979
## 16            sydney            sydney   -33.85    150.93     2006
## 17          genesee1          genesee1    43.09    -83.63     2004
## 18          genesee2          genesee2    43.09    -83.63     2005
## 19          columbus          columbus    39.98    -82.99     2003
## 20             bronx             bronx    40.82    -73.92     2005
##      date_min year_max   date_max      onset before_onset
## 1  2004-01-01     2014 2014-12-31 2004-10-14   2004-09-30
## 2  2002-01-01     2012 2012-12-31 2012-08-26   2012-08-12
## 3  2002-01-01     2012 2012-12-31 2012-07-18   2012-07-04
## 4  2002-01-01     2012 2012-12-31 2012-07-02   2012-06-18
## 5  2002-01-01     2012 2012-12-31 2012-05-01   2012-04-17
## 6  1992-01-01     2002 2002-12-31 2002-07-18   2002-07-04
## 7  1993-01-01     2003 2003-12-31 2003-11-28   2003-11-14
## 8  1996-01-01     2006 2006-12-31 2006-06-01   2006-05-18
## 9  1995-01-01     2005 2005-12-31 2005-05-26   2005-05-12
## 10 1995-01-01     2005 2005-12-31 2005-05-12   2005-04-28
## 11 1992-01-01     2002 2002-12-31 2002-07-30   2002-07-16
## 12 1991-01-01     2001 2001-12-31 2001-06-26   2001-06-12
## 13 1990-01-01     2000 2000-12-31 2000-04-17   2000-04-03
## 14 1989-01-01     1999 1999-12-31 1999-02-25   1999-02-11
## 15 1979-01-01     1989 1989-12-31 1989-01-01   1988-12-18
## 16 2006-01-01     2016 2016-12-31 2016-04-25   2016-04-11
## 17 2004-01-01     2014 2014-12-31 2014-06-06   2014-05-23
## 18 2005-01-01     2015 2015-12-31 2015-05-04   2015-04-20
## 19 2003-01-01     2013 2013-12-31 2013-07-09   2013-06-25
## 20 2005-01-01     2015 2015-12-31 2015-07-12   2015-06-28
id file_id latitude longitude year_min date_min year_max date_max onset before_onset
portugal portugal 38.96 -8.99 2004 2004-01-01 2014 2014-12-31 2004-10-14 2004-09-30
pittsburgh pittsburgh 40.43 -79.98 2002 2002-01-01 2012 2012-12-31 2012-08-26 2012-08-12
quebec quebec 46.85 -71.34 2002 2002-01-01 2012 2012-12-31 2012-07-18 2012-07-04
stoke-on-trent stoke_on_trent 53.02 -2.15 2002 2002-01-01 2012 2012-12-31 2012-07-02 2012-06-18
edinburgh edinburgh 55.94 -3.20 2002 2002-01-01 2012 2012-12-31 2012-05-01 2012-04-17
miyazaki miyazaki 31.89 131.34 1992 1992-01-01 2002 2002-12-31 2002-07-18 2002-07-04
pas-de-calais pas_de_calais 50.51 2.37 1993 1993-01-01 2003 2003-12-31 2003-11-28 2003-11-14
pamplona pamplona 42.81 -1.65 1996 1996-01-01 2006 2006-12-31 2006-06-01 2006-05-18
rapid city rapid_city 44.06 -103.22 1995 1995-01-01 2005 2005-12-31 2005-05-26 2005-05-12
sarpsborg sarpsborg 59.28 11.08 1995 1995-01-01 2005 2005-12-31 2005-05-12 2005-04-28
barrow-in-furness barrow_in_furness 54.10 -3.22 1992 1992-01-01 2002 2002-12-31 2002-07-30 2002-07-16
murcia murcia 37.98 -1.12 1991 1991-01-01 2001 2001-12-31 2001-06-26 2001-06-12
melbourne melbourne -37.86 145.07 1990 1990-01-01 2000 2000-12-31 2000-04-17 2000-04-03
bovenkarspel bovenkarspel 52.70 5.24 1989 1989-01-01 1999 1999-12-31 1999-02-25 1999-02-11
london london 51.52 -0.10 1979 1979-01-01 1989 1989-12-31 1989-01-01 1988-12-18
sydney sydney -33.85 150.93 2006 2006-01-01 2016 2016-12-31 2016-04-25 2016-04-11
genesee1 genesee1 43.09 -83.63 2004 2004-01-01 2014 2014-12-31 2014-06-06 2014-05-23
genesee2 genesee2 43.09 -83.63 2005 2005-01-01 2015 2015-12-31 2015-05-04 2015-04-20
columbus columbus 39.98 -82.99 2003 2003-01-01 2013 2013-12-31 2013-07-09 2013-06-25
bronx bronx 40.82 -73.92 2005 2005-01-01 2015 2015-12-31 2015-07-12 2015-06-28

The next function will download information from all of the stations. It only needs to be downloaded once per session. It will take a couple minutes to download.

I created a loop to get a list of the stations within 30 km of the location.

station_data <- ghcnd_stations()[[1]]
df <- list()
for(i in 1:length(outbreak_loc$id))
  {
    df[[i]] <- (meteo_nearby_stations(lat_lon_df = outbreak_loc[i,],
                                    station_data = station_data,
                                    var = c("PRCP","TAVG","TMAX","TMIN",
                                            "AWND","MDPR"),
                                    year_min = outbreak_loc[i, "year_min"],
                                    year_max = outbreak_loc[i, "year_max"],
                                    radius = 30)[[1]])
  }

names(df) <- outbreak_loc$id
stations <- df
saveRDS(stations, file = "stations.RData")
## $portugal
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $pittsburgh
##             id                          name latitude longitude  distance
## 1  US1PAAL0014          PA PITTSBURGH 1.6 SW  40.4226  -79.9974  1.687108
## 2  US1PAAL0017           PA WHITEHALL 1.0 SW  40.3475  -80.0022  9.364279
## 3  USW00014762 PA PITTSBURGH ALLEGHENY CO AP  40.3547  -79.9217  9.720301
## 4  US1PAAL0011        PA WEST MIFFLIN 1.3 SW  40.3466  -79.9283 10.255413
## 5  US1PAAL0031      PA SCOTT TOWNSHIP 1.3 NW  40.3978  -80.0967 10.508788
## 6  USC00360861            PA BRADDOCK LOCK 2  40.3917  -79.8594 11.063215
## 7  US1PAAL0009                 PA PATHFINDER  40.3416  -80.0485 11.414110
## 8  USC00362574      PA EMSWORTH L/D OHIO RVR  40.5019  -80.0833 11.844197
## 9  US1PAAL0016            PA GLENSHAW 1.3 NW  40.5488  -79.9800 13.209957
## 10 USC00365573                 PA MCKEESPORT  40.3392  -79.8603 14.308275
## 11 US1PAAL0008    PA UPPER ST. CLAIR 1.7 WNW  40.3412  -80.1026 14.329100
## 12 US1PAAL0020         PA ALLISON PARK 0.7 W  40.5610  -79.9708 14.587294
## 13 US1PAAL0023 PA SOUTH PARK TOWNSHIP 0.2 NW  40.2989  -79.9970 14.648635
## 14 US1PAAL0003         PA SOUTH FAYETTE 2 SE  40.3381  -80.1159 15.392161
## 15 US1PAAL0001         PA BRIDGEVILLE 1.4 SW  40.3417  -80.1229 15.584945
## 16 US1PAAL0004           PA PENN HILLS 1.5 E  40.4759  -79.7982 16.207171
## 17 USC00360022           PA ACMETONIA LOCK 3  40.5361  -79.8153 18.254228
## 18 US1PAAL0006           PA MCDONALD 2.5 ENE  40.3822  -80.1871 18.323293
## 19 US1PAWS0005            PA MCMURRAY 0.2 NE  40.2831  -80.0857 18.628831
## 20 USW00094823         PA PITTSBURGH INTL AP  40.4847  -80.2144 20.743637
## 21 US1PAAL0030          PA CARNOT-MOON 0.9 S  40.5061  -80.2119 21.364459
## 22 USC00366111           PA MURRYSVILLE 2 SW  40.4119  -79.7244 21.730660
## 23 US1PAWT0001        PA NORTH IRWIN 2.5 WSW  40.3243  -79.7556 22.348628
## 24 USC00365918              PA MOON TOWNSHIP  40.5319  -80.2172 23.040372
## 25 USC00363343      PA GLENWILLARD DASHIELDS  40.5514  -80.2167 24.143027
## 26 US1PAWT0010        PA MURRYSVILLE 1.5 WSW  40.4317  -79.6813 25.282776
## 27 US1PAAL0012        PA SOUTH HEIGHTS 1.5 S  40.5533  -80.2379 25.760534
## 
## $quebec
##            id                         name latitude longitude  distance
## 1 CA007011309  QC CHARLESBOURG PARC ORLEAN  46.8667  -71.2667  5.874619
## 2 CA007016294 QC QUEBEC/JEAN LESAGE INTL A  46.8000  -71.3833  6.462488
## 3 CA00701S001   QC QUEBEC/JEAN LESAGE INTL  46.8000  -71.3833  6.462488
## 4 CA00701Q004        QC STE-FOY (U. LAVAL)  46.7833  -71.2833  8.580380
## 5 CA007010565                  QC BEAUPORT  46.8333  -71.2000 10.808994
## 6 CA007018572                QC VALCARTIER  46.9000  -71.5000 13.372471
## 7 CA007024254                    QC LAUZON  46.8167  -71.1000 18.628731
## 8 CA007020567                QC BEAUSEJOUR  46.6667  -71.1667 24.283856
## 9 CA007041330            QC CHATEAU RICHER  46.9667  -71.0333 26.668362
## 
## $`stoke-on-trent`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $edinburgh
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $miyazaki
##            id       name latitude longitude  distance
## 1 JA000047830   MIYAZAKI   31.933   131.417  8.699733
## 2 JA000047829 MIYAKONOJO   31.733   131.083 29.908209
## 
## $`pas-de-calais`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $pamplona
##            id                    name latitude longitude distance
## 1 SPE00120350 PAMPLONA (OBSERVATORIO)  42.8175   -1.6364 1.387848
## 2 SPE00120359                PAMPLONA  42.7767   -1.6500 3.702791
## 
## $`rapid city`
##             id                         name latitude longitude  distance
## 1  USC00396948            SD RAPID CITY WFO  44.0728 -103.2108  1.601898
## 2  USC00396947            SD RAPID CITY 4NW  44.1150 -103.2828  7.909484
## 3  USW00024090        SD RAPID CITY RGNL AP  44.0433 -103.0536 13.427258
## 4  USC00394343            SD JOHNSON SIDING  44.0839 -103.4342 17.317536
## 5  USR0000SBAK   SD BAKER PARK SOUTH DAKOTA  43.9792 -103.4250 18.692682
## 6  USC00396427               SD PACTOLA DAM  44.0622 -103.4819 20.928415
## 7  USC00394556                  SD KEYSTONE  43.9039 -103.4100 23.073539
## 8  USR0000SNEM         SD NEMO SOUTH DAKOTA  44.1917 -103.5097 27.370234
## 9  USC00395870      SD MT RUSHMORE NATL MEM  43.8769 -103.4578 27.869317
## 10 USR0000SMRU SD MT. RUSHMORE SOUTH DAKOTA  43.8750 -103.4583 28.051427
## 11 USC00393775             SD HERMOSA 3 SSW  43.8069 -103.2131 28.148859
## 
## $sarpsborg
##            id              name latitude longitude  distance
## 1 NOE00109849         SARPSBORG  59.2856   11.1144  2.050694
## 2 NOE00134298            FLOTER  59.4964   11.0131 24.358920
## 3 NOE00100575            HALDEN  59.1225   11.3883 24.795447
## 4 NOE00109786            HVALER  59.0358   11.0517 27.201683
## 5 NOE00109876 MOSS BRANNSTASJON  59.4428   10.6842 28.822789
## 6 NOE00109867              MOSS  59.4339   10.6667 29.008867
## 
## $`barrow-in-furness`
## [1] id        name      latitude  longitude distance 
## <0 rows> (or 0-length row.names)
## 
## $murcia
##            id                name latitude longitude distance
## 1 SPE00120323              MURCIA  38.0028   -1.1692 5.001690
## 2 SPE00120332 MURCIA/ALCANTARILLA  37.9578   -1.2294 9.902609
## 
## $melbourne
##             id                           name latitude longitude  distance
## 1  ASN00086018         CAULFIELD (RACECOURSE) -37.8795  145.0368  3.632396
## 2  ASN00086304      HAWTHORN (SCOTCH COLLEGE) -37.8361  145.0294  4.446429
## 3  ASN00086095           PRAHRAN (COMO HOUSE) -37.8376  145.0048  6.243145
## 4  ASN00086088 OAKLEIGH (METROPOLITAN GOLF CL -37.9142  145.0935  6.369850
## 5  ASN00086012     BOX HILL AGED MENS RETREAT -37.8364  145.1364  6.393542
## 6  ASN00086006                      BENTLEIGH -37.9279  145.0749  7.562369
## 7  ASN00086033 BRIGHTON (DENDY PARK BOWLING C -37.9252  145.0254  8.238821
## 8  ASN00086232    MELBOURNE BOTANICAL GARDENS -37.8303  144.9767  8.833034
## 9  ASN00086279                      NORTHCOTE -37.7797  145.0314  9.551015
## 10 ASN00086316     VERMONT TRANSPORT RESEARCH -37.8587  145.1847 10.070617
## 11 ASN00086071      MELBOURNE REGIONAL OFFICE -37.8075  144.9700 10.545355
## 12 ASN00086020     CHELTENHAM KINGSTON CENTRE -37.9551  145.0782 10.599081
## 13 ASN00086303    GLEN WAVERLEY (GOLF COURSE) -37.8886  145.1928 11.237859
## 14 ASN00086074                        MITCHAM -37.8219  145.1906 11.406147
## 15 ASN00086260                HEIDELBERG MMBW -37.7567  145.0533 11.579751
## 16 ASN00086378                      BRUNSWICK -37.7667  144.9797 13.059611
## 17 ASN00086111          SPRINGVALE NECROPOLIS -37.9445  145.1764 13.245203
## 18 ASN00086369           SPRINGVALE (SANDOWN) -37.9535  145.1655 13.352636
## 19 ASN00086068             VIEWBANK (ARPANSA) -37.7408  145.0972 13.468157
## 20 ASN00086077              MOORABBIN AIRPORT -37.9800  145.0964 13.542852
## 21 ASN00086146                      BEAUMARIS -37.9771  145.0273 13.548961
## 22 ASN00086362   DONCASTER (MANNINGHAM DEPOT) -37.7494  145.1703 15.129262
## 23 ASN00086351  BUNDOORA (LATROBE UNIVERSITY) -37.7163  145.0453 16.125457
## 24 ASN00086039          FLEMINGTON RACECOURSE -37.7915  144.9067 16.239778
## 25 ASN00086104    SCORESBY RESEARCH INSTITUTE -37.8710  145.2561 16.382188
## 26 ASN00086096              PRESTON RESERVOIR -37.7214  145.0059 16.408663
## 27 ASN00086230                      BAYSWATER -37.8372  145.2558 16.509685
## 28 ASN00086379                 RINGWOOD NORTH -37.7917  145.2433 17.010511
## 29 ASN00086101                       RINGWOOD -37.8000  145.2500 17.158761
## 30 ASN00086313                     WARRANDYTE -37.7469  145.2098 17.578887
## 31 ASN00086347       YARRA RIVER @ WARRANDYTE -37.7417  145.2167 18.416453
## 32 ASN00086224                      DANDENONG -37.9785  145.2235 18.839479
## 33 ASN00086035                         ELTHAM -37.7011  145.1547 19.172878
## 34 ASN00087038 MARIBYRNONG EXPLOSIVES FACTORY -37.7750  144.8767 19.432886
## 35 ASN00086324       FERNTREE GULLY (PROBERT) -37.8797  145.2964 19.993316
## 36 ASN00086027        CROYDON (SAMUEL STREET) -37.7903  145.2812 20.103933
## 37 ASN00086234        CROYDON (COUNCIL DEPOT) -37.7869  145.2847 20.535018
## 38 ASN00086038               ESSENDON AIRPORT -37.7276  144.9066 20.564229
## 39 ASN00087131          ALTONA (CITY OFFICES) -37.8633  144.8261 21.414594
## 40 ASN00087148    SUNSHINE (CITY OF BRINBANK) -37.7928  144.8344 22.000521
## 41 ASN00086250                         PLENTY -37.6600  145.1244 22.747356
## 42 ASN00086256                    FERNY CREEK -37.8833  145.3333 23.256162
## 43 ASN00086210              BONBEACH (CARRUM) -38.0651  145.1294 23.393049
## 44 ASN00086372       FERNY CREEK (DUNNS HILL) -37.8775  145.3364 23.465245
## 45 ASN00086365                    MOOROOLBARK -37.7792  145.3197 23.701976
## 46 ASN00086251            UPWEY SHIRE COUNCIL -37.9144  145.3317 23.749365
## 47 ASN00086243           MOUNT DANDENONG GTV9 -37.8306  145.3500 24.802430
## 48 ASN00086059                KANGAROO GROUND -37.6830  145.2518 25.351527
## 49 ASN00086254       CARRUM DOWNS SEWER WORKS -38.0783  145.1733 25.907856
## 50 ASN00086036                         EPPING -37.6312  144.9846 26.526359
## 51 ASN00086066                       LILYDALE -37.7488  145.3416 26.875070
## 52 ASN00086076                       MONTROSE -37.8019  145.3675 26.914609
## 53 ASN00086085 NARRE WARREN NORTH (NARRE WARR -37.9897  145.3356 27.399212
## 54 ASN00087031                  LAVERTON RAAF -37.8565  144.7566 27.516720
## 55 ASN00087027               KEILOR (ARUNDEL) -37.6942  144.8342 27.737658
## 56 ASN00087177            LAVERTON COMPARISON -37.8633  144.7456 28.480727
## 57 ASN00086305            GREENVALE RESERVOIR -37.6369  144.9072 28.640881
## 58 ASN00086384   MELBOURNE AIRPORT COMPARISON -37.6750  144.8419 28.725743
## 59 ASN00087015                         KEILOR -37.7025  144.8072 28.984935
## 
## $bovenkarspel
##             id          name latitude longitude  distance
## 1  NLE00101917     ENKHUIZEN  52.6917    5.2944  3.780363
## 2  NLE00109144   HOOGKARSPEL  52.6867    5.1669  5.143626
## 3  NLE00101928     MEDEMBLIK  52.7781    5.1014 12.746887
## 4  NLE00100501         HOORN  52.6444    5.0681 13.136277
## 5  NLE00102479      BERKHOUT  52.6428    4.9789 18.718869
## 6  NLE00109146      HOOGWOUD  52.7281    4.9608 19.065006
## 7  NLE00109174   KREILEROORD  52.8619    5.0953 20.464698
## 8  NLE00102134      STAVOREN  52.8967    5.3831 23.894441
## 9  NLE00109232         OBDAM  52.6775    4.8769 24.600539
## 10 NLE00109054          EDAM  52.5114    5.0467 24.701897
## 11 NLE00109250    OUDEMIRDUM  52.8608    5.5078 25.379621
## 12 NLE00109162       KOLHORN  52.7914    4.8919 25.540549
## 13 NLE00109354 WEST BEEMSTER  52.5817    4.9028 26.281190
## 14 NLE00101948     TOLLEBEEK  52.6719    5.6300 26.472774
## 15 NLE00101930     DEN OEVER  52.9217    5.0383 28.133541
## 16 NLE00101932        MARKEN  52.4600    5.1078 28.142012
## 17 NLE00109028     DE HAUKES  52.8783    4.9408 28.246661
## 18 NLE00109254     PURMEREND  52.5125    4.9506 28.575998
## 
## $london
##            id     name latitude longitude distance
## 1 UKM00003772 HEATHROW   51.478    -0.461 25.42177
## 
## $sydney
##             id                           name latitude longitude  distance
## 1  ASN00067019             PROSPECT RESERVOIR -33.8193  150.9127  3.769152
## 2  ASN00067017   GREYSTANES (BATHURST STREET) -33.8136  150.9392  4.135739
## 3  ASN00067070   MERRYLANDS (WELSFORD STREET) -33.8269  150.9767  5.020100
## 4  ASN00067114 ABBOTSBURY (FAIRFIELD CITY FAR -33.8667  150.8611  6.627566
## 5  ASN00067119 HORSLEY PARK EQUESTRIAN CENTRE -33.8511  150.8567  6.770114
## 6  ASN00067110  SEVEN HILLS  (RADIO FM 103.2) -33.7858  150.9236  7.163157
## 7  ASN00067026       SEVEN HILLS (COLLINS ST) -33.7704  150.9318  8.852678
## 8  ASN00067020 LIVERPOOL (MICHAEL WENDEN CENT -33.9214  150.8861  8.913714
## 9  ASN00066137          BANKSTOWN AIRPORT AWS -33.9181  150.9864  9.189477
## 10 ASN00066134       GRANVILLE SHELL REFINERY -33.8322  151.0340  9.806921
## 11 ASN00066168 MILPERRA BRIDGE (GEORGES RIVER -33.9289  150.9831 10.049571
## 12 ASN00067042    KINGS LANGLEY (SOLANDER RD) -33.7610  150.9498 10.064021
## 13 ASN00067111 NORTH PARRAMATTA (BURNSIDE HOM -33.7931  151.0167 10.206744
## 14 ASN00067109   BAULKHAM HILLS EUCALYPTUS CT -33.7678  150.9814 10.300292
## 15 ASN00066124 PARRAMATTA NORTH (MASONS DRIVE -33.7917  151.0181 10.404864
## 16 ASN00066050           POTTS HILL RESERVOIR -33.8933  151.0346 10.790772
## 17 ASN00066164       ROOKWOOD (HAWTHORNE AVE) -33.8771  151.0577 12.169844
## 18 ASN00067112 NORTH ROCKS (MUIRFIELD GOLF CL -33.7672  151.0186 12.319787
## 19 ASN00066195 SYDNEY OLYMPIC PARK (SYDNEY OL -33.8521  151.0646 12.431978
## 20 ASN00066070          STRATHFIELD GOLF CLUB -33.8805  151.0631 12.748603
## 21 ASN00066054         REVESBY (PATEN STREET) -33.9474  151.0065 12.928587
## 22 ASN00067076   QUAKERS HILL TREATMENT WORKS -33.7366  150.8758 13.567795
## 23 ASN00066185      CARLINGFORD (BARELLAN AV) -33.7801  151.0587 14.205035
## 24 ASN00066191        GLENFIELD (HARROW ROAD) -33.9770  150.9042 14.321038
## 25 ASN00067117       HOLSWORTHY CONTROL RANGE -33.9795  150.9254 14.405998
## 26 ASN00067102          ST CLAIR (JUBA CLOSE) -33.8044  150.7778 14.945410
## 27 ASN00067100     CASTLE HILL (KATHLEEN AVE) -33.7260  150.9944 15.017779
## 28 ASN00067089 WEST PENNANT HILLS (CUMBERLAND -33.7459  151.0402 15.416884
## 29 ASN00067003         COLYTON (CARPENTER ST) -33.7770  150.7877 15.450666
## 30 ASN00067098 WEST PENNANT HILLS  (ORATAVA A -33.7487  151.0449 15.478987
## 31 ASN00066013              CONCORD GOLF CLUB -33.8523  151.0985 15.562401
## 32 ASN00067061         ROSSMORE (SOUTH CREEK) -33.9353  150.7819 16.638119
## 33 ASN00066048             CONCORD (BRAYS RD) -33.8483  151.1105 16.669913
## 34 ASN00067037         SCHOFIELDS BOUNDARY RD -33.6947  150.8868 17.724215
## 35 ASN00066194      CANTERBURY RACECOURSE AWS -33.9057  151.1134 18.028240
## 36 ASN00066148            PEAKHURST GOLF CLUB -33.9700  151.0638 18.179759
## 37 ASN00066034 ABBOTSFORD (BLACKWALL POINT RD -33.8507  151.1295 18.423361
## 38 ASN00067116       WILLMOT (RESOLUTION AVE) -33.7231  150.7997 18.550317
## 39 ASN00066156 MACQUARIE PARK (WILLANDRA VILL -33.7791  151.1121 18.579019
## 40 ASN00066047   PENNANT HILLS (YARRARA ROAD) -33.7324  151.0767 18.835558
## 41 ASN00066190   INGLEBURN (SACKVILLE STREET) -34.0117  150.8647 18.962689
## 42 ASN00067108             BADGERYS CREEK AWS -33.8969  150.7281 19.355573
## 43 ASN00066181       OATLEY (WORONORA PARADE) -33.9766  151.0766 19.523783
## 44 ASN00066004            BEXLEY BOWLING CLUB -33.9430  151.1098 19.553323
## 45 ASN00067086      DURAL (OLD NORTHERN ROAD) -33.6867  151.0250 20.170027
## 46 ASN00066036         MARRICKVILLE GOLF CLUB -33.9186  151.1402 20.849104
## 47 ASN00066131          RIVERVIEW OBSERVATORY -33.8258  151.1556 21.009524
## 48 ASN00067104          BOX HILL (HYNDS ROAD) -33.6617  150.9000 21.120894
## 49 ASN00066189       WEST PYMBLE (WYUNA ROAD) -33.7693  151.1380 21.209116
## 50 ASN00067084  ORCHARD HILLS TREATMENT WORKS -33.8020  150.7069 21.288391
## 51 ASN00066204  OYSTER BAY (GREEN POINT ROAD) -34.0009  151.0738 21.391107
## 52 ASN00066158 TURRAMURRA (KISSING POINT ROAD -33.7366  151.1271 22.152621
## 53 ASN00066120               GORDON GOLF CLUB -33.7617  151.1462 22.258367
## 54 ASN00066078          LUCAS HEIGHTS (ANSTO) -34.0517  150.9800 22.897282
## 55 ASN00067015           BRINGELLY (MARYLAND) -33.9696  150.7250 23.124628
## 56 ASN00068160 CAMPBELLTOWN (KENTLYN (GEORGES -34.0542  150.8772 23.222412
## 57 ASN00068250      CAMDEN VALLEY GOLF RESORT -34.0128  150.7675 23.504569
## 58 ASN00066157      PYMBLE (CANISIUS COLLEGE) -33.7371  151.1521 24.058869
## 59 ASN00066058     SANS SOUCI (PUBLIC SCHOOL) -33.9942  151.1292 24.391064
## 60 ASN00067022      GALSTON (ROWLAND VILLAGE) -33.6550  151.0553 24.583503
## 61 ASN00068231          RUSE (DENISON STREET) -34.0630  150.8489 24.837610
## 62 ASN00066114   NORTH TURRAMURRA (DRYDEN RD) -33.7179  151.1470 24.858762
## 63 ASN00066037             SYDNEY AIRPORT AMO -33.9465  151.1731 24.870760
## 64 ASN00066062      SYDNEY (OBSERVATORY HILL) -33.8607  151.2050 25.421751
## 65 ASN00066011         CHATSWOOD BOWLING CLUB -33.8000  151.2000 25.553205
## 66 ASN00067115 GLENMORE PARK  (CARTWRIGHT CL) -33.7826  150.6619 25.877100
## 67 ASN00066006         SYDNEY BOTANIC GARDENS -33.8662  151.2160 26.470162
## 68 ASN00066080   CASTLE COVE (ROSEBRIDGE AVE) -33.7809  151.2044 26.489163
## 69 ASN00066176  AUDLEY  (ROYAL NATIONAL PARK) -34.0658  151.0567 26.689966
## 70 ASN00067029           WALLACIA POST OFFICE -33.8637  150.6410 26.729649
## 71 ASN00066206      ST IVES (RICHMOND AVENUE) -33.7096  151.1730 27.351861
## 72 ASN00067113              PENRITH LAKES AWS -33.7195  150.6783 27.416523
## 73 ASN00068257     CAMPBELLTOWN (MOUNT ANNAN) -34.0615  150.7735 27.594122
## 74 ASN00066073            RANDWICK RACECOURSE -33.9105  151.2276 28.284461
## 75 ASN00068254     MOUNT ANNAN BOTANIC GARDEN -34.0673  150.7678 28.418732
## 76 ASN00066160                CENTENNIAL PARK -33.8959  151.2341 28.535385
## 77 ASN00067031           WINDSOR BOWLING CLUB -33.6100  150.8151 28.724326
## 78 ASN00066188         BELROSE (EVELYN PLACE) -33.7402  151.2173 29.221247
## 79 ASN00066052          RANDWICK BOWLING CLUB -33.9096  151.2419 29.545881
## 80 ASN00066086                   CRONULLA STP -34.0313  151.1642 29.549535
## 81 ASN00067010     GLENORIE (OLD NORTHERN RD) -33.5908  151.0094 29.742533
## 
## $genesee1
##             id                    name latitude longitude  distance
## 1  USC00201150            MI BURTON 4N  43.0675  -83.5919  3.979309
## 2  US1MIGN0010       MI BURTON 0.9 NNW  43.0085  -83.6274  9.064849
## 3  US1MIGN0008 MI MOUNT MORRIS 3.1 WSW  43.1057  -83.7580 10.538333
## 4  US1MIGN0014       MI DAVISON 3.3 SW  43.0003  -83.5684 11.159857
## 5  US1MIGN0005      MI DAVISON 0.7 SSW  43.0219  -83.5246 11.431375
## 6  USC00202851            MI FLINT 7 W  43.0378  -83.7694 12.725462
## 7  USC00201645                 MI CLIO  43.1794  -83.7369 13.193328
## 8  US1MIGN0009        MI BURTON 3.3 SW  42.9613  -83.6636 14.569099
## 9  USW00014826 MI FLINT BISHOP INTL AP  42.9667  -83.7494 16.797891
## 10 US1MIGN0023  MI GRAND BLANC 3.8 WNW  42.9440  -83.6886 16.919078
## 11 US1MISG0004    MI BIRCH RUN 2.6 ESE  43.2291  -83.7470 18.146491
## 12 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE  42.9897  -83.8166 18.824526
## 13 US1MIGN0015        MI FLINT 6.4 SSW  42.9326  -83.7210 19.001794
## 14 US1MIGN0018   MI GRAND BLANC 0.7 SE  42.9187  -83.6079 19.132279
## 15 USC00203278             MI GOODRICH  42.9164  -83.5097 21.640755
## 16 USC00204659            MI LAPEER 2W  43.0581  -83.3606 22.167570
## 17 US1MIGN0022   MI GRAND BLANC 2.9 SE  42.8909  -83.5858 22.428899
## 18 US1MIGN0004      MI MONTROSE 0.4 NW  43.1794  -83.8987 23.962697
## 19 USC00205488      MI MILLINGTON 3 SE  43.2836  -83.4792 24.756898
## 20 US1MILP0003       MI LAPEER 1.1 SSW  43.0316  -83.3293 25.277892
## 21 USC00204655          MI LAPEER WWTP  43.0608  -83.3075 26.394851
## 22 USC00202955      MI FRANKENMUTH 1SE  43.3194  -83.7161 26.445482
## 
## $genesee2
##             id                    name latitude longitude  distance
## 1  USC00201150            MI BURTON 4N  43.0675  -83.5919  3.979309
## 2  US1MIGN0010       MI BURTON 0.9 NNW  43.0085  -83.6274  9.064849
## 3  US1MIGN0008 MI MOUNT MORRIS 3.1 WSW  43.1057  -83.7580 10.538333
## 4  US1MIGN0014       MI DAVISON 3.3 SW  43.0003  -83.5684 11.159857
## 5  US1MIGN0005      MI DAVISON 0.7 SSW  43.0219  -83.5246 11.431375
## 6  USC00202851            MI FLINT 7 W  43.0378  -83.7694 12.725462
## 7  US1MIGN0024          MI CLIO 0.4 SW  43.1725  -83.7423 12.930649
## 8  USC00201645                 MI CLIO  43.1794  -83.7369 13.193328
## 9  US1MIGN0009        MI BURTON 3.3 SW  42.9613  -83.6636 14.569099
## 10 USW00014826 MI FLINT BISHOP INTL AP  42.9667  -83.7494 16.797891
## 11 US1MIGN0023  MI GRAND BLANC 3.8 WNW  42.9440  -83.6886 16.919078
## 12 US1MISG0004    MI BIRCH RUN 2.6 ESE  43.2291  -83.7470 18.146491
## 13 US1MIGN0020 MI SWARTZ CREEK 2.0 NNE  42.9897  -83.8166 18.824526
## 14 US1MIGN0015        MI FLINT 6.4 SSW  42.9326  -83.7210 19.001794
## 15 US1MIGN0018   MI GRAND BLANC 0.7 SE  42.9187  -83.6079 19.132279
## 16 USC00203278             MI GOODRICH  42.9164  -83.5097 21.640755
## 17 USC00204659            MI LAPEER 2W  43.0581  -83.3606 22.167570
## 18 US1MIGN0022   MI GRAND BLANC 2.9 SE  42.8909  -83.5858 22.428899
## 19 US1MIGN0004      MI MONTROSE 0.4 NW  43.1794  -83.8987 23.962697
## 20 USC00205488      MI MILLINGTON 3 SE  43.2836  -83.4792 24.756898
## 21 US1MILP0003       MI LAPEER 1.1 SSW  43.0316  -83.3293 25.277892
## 22 USC00204655          MI LAPEER WWTP  43.0608  -83.3075 26.394851
## 23 USC00202955      MI FRANKENMUTH 1SE  43.3194  -83.7161 26.445482
## 
## $columbus
##             id                              name latitude longitude
## 1  US1OHFR0018               OH COLUMBUS 2.4 WNW  39.9977  -83.0323
## 2  US1OHFR0025               OH COLUMBUS 2.8 WSW  39.9804  -83.0397
## 3  US1OHFR0003        OH GRANDVIEW HEIGHTS 0.1 N  39.9810  -83.0401
## 4  USC00331785                  OH COLUMBUS WCMH  40.0250  -83.0269
## 5  US1OHFR0034                OH COLUMBUS 3.6 NW  40.0191  -83.0437
## 6  US1OHFR0020                OH COLUMBUS 3.5 NE  40.0287  -82.9477
## 7  US1OHFR0001          OH UPPER ARLINGTON 0.9 E  40.0279  -83.0543
## 8  US1OHFR0021           OH MARBLE CLIFF 1.1 WNW  39.9931  -83.0786
## 9  US1OHFR0007        OH UPPER ARLINGTON 1.3 SSW  40.0112  -83.0832
## 10 USW00014821 OH COLUMBUS PORT COLUMBUS INTL AP  39.9914  -82.8808
## 11 USC00331783       OH COLUMBUS-VALLEY CROSSING  39.9047  -82.9200
## 12 US1OHFR0012        OH UPPER ARLINGTON 2.4 NNW  40.0604  -83.0815
## 13 USC00331777        OH COLUMBUS-HAP CREMEAN WP  40.0603  -82.8942
## 14 US1OHFR0024               OH COLUMBUS 9.3 NNE  40.0925  -82.9582
## 15 USW00004804    OH COLUMBUS OHIO STATE UNIV AP  40.0781  -83.0781
## 16 US1OHFR0037             OH REYNOLDSBURG 1.6 W  39.9588  -82.8294
## 17 US1OHFR0016                 OH DUBLIN 3.7 ESE  40.0923  -83.0725
## 18 US1OHFR0022                 OH GALLOWAY 3.1 N  39.9561  -83.1592
## 19 USC00331779          OH COLUMBUS-PARSONS AVE.  39.8469  -82.9872
## 20 USC00338951                    OH WESTERVILLE  40.1264  -82.9433
## 21 US1OHFR0010            OH WESTERVILLE 0.2 WNW  40.1226  -82.9213
## 22 US1OHFR0030                 OH HILLIARD 1.8 W  40.0344  -83.1768
## 23 US1OHFR0008             OH NEW ALBANY 2.8 SSE  40.0403  -82.7980
## 24 US1OHFR0002                 OH DUBLIN 3.2 ENE  40.1299  -83.0742
## 25 US1OHLC0002              OH PATASKALA 4.4 WNW  40.0273  -82.7490
## 26 US1OHFF0005           OH PICKERINGTON 2.7 NNE  39.9263  -82.7469
## 27 US1OHDL0002              OH WESTERVILLE 4.0 N  40.1790  -82.9256
## 28 US1OHFR0023             OH HARRISBURG 3.7 WNW  39.8378  -83.2321
## 29 US1OHLC0011               OH PATASKALA 2.0 NE  40.0240  -82.6511
##     distance
## 1   4.106137
## 2   4.234920
## 3   4.270197
## 4   5.909011
## 5   6.310790
## 6   6.504242
## 7   7.639623
## 8   7.687720
## 9   8.664212
## 10  9.389591
## 11 10.282003
## 12 11.858973
## 13 12.094496
## 14 12.799033
## 15 13.238469
## 16 13.887609
## 17 14.326951
## 18 14.662096
## 19 14.801971
## 20 16.757176
## 21 16.900247
## 22 17.021110
## 23 17.673463
## 24 18.143413
## 25 21.190424
## 26 21.564691
## 27 22.796070
## 28 26.008132
## 29 29.278406
## 
## $bronx
##             id                        name latitude longitude  distance
## 1  USW00014732    NY NEW YORK LAGUARDIA AP  40.7794  -73.8803  5.616756
## 2  USW00094728    NY NEW YORK CNTRL PK TWR  40.7789  -73.9692  6.167420
## 3  USC00300961                    NY BRONX  40.8369  -73.8494  6.230297
## 4  US1NJBG0018   NJ PALISADES PARK 0.2 WNW  40.8481  -74.0002  7.435652
## 5  US1NJBG0003            NJ TENAFLY 1.3 W  40.9147  -73.9775 11.587163
## 6  US1NYQN0002    NY MIDDLE VILLAGE 0.5 SW  40.7145  -73.8819 12.161951
## 7  USW00094741             NJ TETERBORO AP  40.8500  -74.0614 12.354793
## 8  US1NJBG0001       NJ BERGENFIELD 0.3 SW  40.9213  -74.0020 13.206762
## 9  US1NJBG0012        NJ WOOD RIDGE 0.6 SE  40.8420  -74.0830 13.930427
## 10 US1NJBG0033       NJ WOOD RIDGE 0.6 NNW  40.8536  -74.0943 15.131881
## 11 US1NYWC0009       NY NEW ROCHELLE 1.3 S  40.9040  -73.7770 15.226895
## 12 US1NJBG0013         NJ RUTHERFORD 1.2 N  40.8373  -74.1065 15.809146
## 13 US1NYKN0025          NY BROOKLYN 3.1 NW  40.6846  -73.9867 16.069963
## 14 US1NJBG0031         NJ DEMAREST 0.6 NNW  40.9628  -73.9600 16.230719
## 15 US1NJBG0002   NJ SADDLE BROOK TWP 0.6 E  40.9027  -74.0834 16.534408
## 16 US1NJBG0011   NJ NORTH ARLINGTON 0.7 NE  40.7944  -74.1190 16.988985
## 17 US1NJBG0008 NJ SADDLE BROOK TWP 0.3 NNE  40.9071  -74.0934 17.505118
## 18 USC00286146              NJ NEW MILFORD  40.9611  -74.0158 17.635536
## 19 US1NJBG0015  NJ NORTH ARLINGTON 0.7 WNW  40.7915  -74.1398 18.769309
## 20 US1NJHD0002            NJ KEARNY 1.7 NW  40.7729  -74.1409 19.318492
## 21 US1NJBG0005         NJ WESTWOOD 0.8 ESE  40.9830  -74.0159 19.836072
## 22 US1NJBG0010     NJ RIVER VALE TWP 1.5 S  40.9915  -74.0123 20.587159
## 23 US1NYNS0007        NY FLORAL PARK 0.4 W  40.7230  -73.7110 20.642010
## 24 US1NJHD0001           NJ HARRISON 0.3 N  40.7480  -74.1518 21.094543
## 25 USC00283704                 NJ HARRISON  40.7514  -74.1567 21.338273
## 26 US1NJBG0020          NJ PARAMUS 1.8 NNW  40.9682  -74.0902 21.822558
## 27 USC00302129      NY DOBBS FERRY-ARDSLEY  41.0072  -73.8344 22.023427
## 28 US1NJBG0017        NJ GLEN ROCK 0.7 SSE  40.9511  -74.1183 22.145027
## 29 US1NJES0020         NJ BLOOMFIELD 1.7 S  40.7850  -74.1885 22.932509
## 30 US1NYKN0003          NY BROOKLYN 2.4 SW  40.6194  -73.9859 22.986706
## 31 US1NYWC0005         NY HARRISON 4.1 SSW  40.9639  -73.7232 23.014851
## 32 USC00307587                NY SEA CLIFF  40.8506  -73.6483 23.109762
## 33 US1NJBG0037        NJ GLEN ROCK 0.4 WNW  40.9614  -74.1328 23.815579
## 34 US1NJPS0014        NJ HAWTHORNE 1.0 SSE  40.9436  -74.1523 23.880745
## 35 USC00289832           NJ WOODCLIFF LAKE  41.0139  -74.0425 23.891667
## 36 US1NJES0015        NJ MONTCLAIR 2.2 NNE  40.8565  -74.2004 23.935385
## 37 USW00094789     NY NEW YORK JFK INTL AP  40.6386  -73.7622 24.159135
## 38 US1NJPS0017     NJ WOODLAND PARK 0.1 NW  40.8918  -74.1960 24.547061
## 39 US1NJPS0005          NJ HAWTHORNE 0.4 S  40.9519  -74.1577 24.787058
## 40 US1NJES0011   NJ CEDAR GROVE TWP 0.9 NE  40.8648  -74.2157 25.368252
## 41 USC00305796        NY NY AVE V BROOKLYN  40.5939  -73.9808 25.658206
## 42 US1NJPS0018           NJ PATERSON 2.0 W  40.9163  -74.2005 25.903423
## 43 USW00014734           NJ NEWARK INTL AP  40.6825  -74.1694 25.982975
## 44 US1NJPS0003 NJ LITTLE FALLS TWP 0.2 NNW  40.8788  -74.2205 26.107408
## 45 US1NJPS0012 NJ LITTLE FALLS TWP 0.5 WNW  40.8796  -74.2270 26.658883
## 46 USC00284887             NJ LITTLE FALLS  40.8858  -74.2261 26.764590
## 47 US1NJES0024    NJ CEDAR GROVE TWP 0.4 W  40.8557  -74.2356 26.845270
## 48 US1NYNS0014          NY LYNBROOK 0.3 NW  40.6623  -73.6780 26.891776
## 49 USC00285503             NJ MIDLAND PARK  40.9939  -74.1453 27.062890
## 50 USC00305377                  NY MINEOLA  40.7328  -73.6183 27.191818
## 51 US1NJPS0004      NJ NORTH HALEDON 0.6 N  40.9713  -74.1856 27.953822
## 52 US1NJES0010        NJ VERONA TWP 0.7 SW  40.8255  -74.2531 28.035405
## 53 US1NJES0021       NJ VERONA TWP 0.6 WSW  40.8305  -74.2539 28.119240
## 54 US1NJES0004   NJ NORTH CALDWELL 0.6 SSE  40.8576  -74.2523 28.265572
## 55 US1NJPS0008        NJ WAYNE TWP 1.1 ESE  40.9412  -74.2267 29.094312
## 56 US1NYWC0003     NY WHITE PLAINS 3.1 NNW  41.0639  -73.7722 29.826697
## 57 US1NYNS0009         NY MILL NECK 1.1 SW  40.8704  -73.5717 29.828998
## 58 US1NYRL0005       NY WEST NYACK 1.3 WSW  41.0835  -73.9930 29.934369

Not all the locations have stations nearby. Therefore, I will omit them from the weather data evaluation using the following code.

has_stations <- sapply(stations, function(x) nrow(x) > 0)
outbreak_loc_true <- outbreak_loc %>%  filter(has_stations)
outbreak_loc_true
##              id      file_id latitude longitude year_min   date_min
## 1    pittsburgh   pittsburgh    40.43    -79.98     2002 2002-01-01
## 2        quebec       quebec    46.85    -71.34     2002 2002-01-01
## 3      miyazaki     miyazaki    31.89    131.34     1992 1992-01-01
## 4      pamplona     pamplona    42.81     -1.65     1996 1996-01-01
## 5    rapid city   rapid_city    44.06   -103.22     1995 1995-01-01
## 6     sarpsborg    sarpsborg    59.28     11.08     1995 1995-01-01
## 7        murcia       murcia    37.98     -1.12     1991 1991-01-01
## 8     melbourne    melbourne   -37.86    145.07     1990 1990-01-01
## 9  bovenkarspel bovenkarspel    52.70      5.24     1989 1989-01-01
## 10       london       london    51.52     -0.10     1979 1979-01-01
## 11       sydney       sydney   -33.85    150.93     2006 2006-01-01
## 12     genesee1     genesee1    43.09    -83.63     2004 2004-01-01
## 13     genesee2     genesee2    43.09    -83.63     2005 2005-01-01
## 14     columbus     columbus    39.98    -82.99     2003 2003-01-01
## 15        bronx        bronx    40.82    -73.92     2005 2005-01-01
##    year_max   date_max      onset before_onset
## 1      2012 2012-12-31 2012-08-26   2012-08-12
## 2      2012 2012-12-31 2012-07-18   2012-07-04
## 3      2002 2002-12-31 2002-07-18   2002-07-04
## 4      2006 2006-12-31 2006-06-01   2006-05-18
## 5      2005 2005-12-31 2005-05-26   2005-05-12
## 6      2005 2005-12-31 2005-05-12   2005-04-28
## 7      2001 2001-12-31 2001-06-26   2001-06-12
## 8      2000 2000-12-31 2000-04-17   2000-04-03
## 9      1999 1999-12-31 1999-02-25   1999-02-11
## 10     1989 1989-12-31 1989-01-01   1988-12-18
## 11     2016 2016-12-31 2016-04-25   2016-04-11
## 12     2014 2014-12-31 2014-06-06   2014-05-23
## 13     2015 2015-12-31 2015-05-04   2015-04-20
## 14     2013 2013-12-31 2013-07-09   2013-06-25
## 15     2015 2015-12-31 2015-07-12   2015-06-28

Using the countyweather codes I can gather the data for each station in a loop. The code gathers the weather data for each stations and averages them. Then I saved all the data as rds. files because they take a long time to gather. The data is saved in a folder I created called “weather_files/”

for(i in which(has_stations))
{
  meteo_df <- meteo_pull_monitors(monitors = stations[[i]]$id,
                                  keep_flags = FALSE,
                                  date_min = outbreak_loc$date_min[i],
                                  date_max = outbreak_loc$date_max[i],
                                  var = c("prcp","snow","snwd","tmax","tmin","tavg"))

  coverage_df <- rnoaa::meteo_coverage(meteo_df, verbose = FALSE)
  filtered <- countyweather:::filter_coverage(coverage_df, 0.90)
  good_monitors <- unique(filtered$id)
  filtered_data <- dplyr::filter(meteo_df, id %in% good_monitors)
  averaged <- countyweather:::ave_daily(filtered_data)

  # For metrics that are reported in tenths of units (precipitation
  # and temperature), divide by 10 to get values in degrees Celsius and
  # millimeters
  which_tenth_units <- which(colnames(averaged) %in%
                               c("prcp", "tavg", "tmax", "tmin"))
  averaged[ , which_tenth_units] <- averaged[ , which_tenth_units] / 10

  file_name <- paste0("weather_files/", outbreak_loc$file_id[i], ".rds")
  saveRDS(averaged, file_name)
}

Now that all of the data is gathered and averaged I can plot the data. The loop will go through the files in order which is in alphabetical order. Therefore I must order my outbreak data frame into alphabetical order too. I will rename this data frame as df_stations for plotting.

##              id      file_id latitude longitude year_min   date_min
## 1  bovenkarspel bovenkarspel    52.70      5.24     1989 1989-01-01
## 2         bronx        bronx    40.82    -73.92     2005 2005-01-01
## 3      columbus     columbus    39.98    -82.99     2003 2003-01-01
## 4      genesee1     genesee1    43.09    -83.63     2004 2004-01-01
## 5      genesee2     genesee2    43.09    -83.63     2005 2005-01-01
## 6        london       london    51.52     -0.10     1979 1979-01-01
## 7     melbourne    melbourne   -37.86    145.07     1990 1990-01-01
## 8      miyazaki     miyazaki    31.89    131.34     1992 1992-01-01
## 9        murcia       murcia    37.98     -1.12     1991 1991-01-01
## 10     pamplona     pamplona    42.81     -1.65     1996 1996-01-01
## 11   pittsburgh   pittsburgh    40.43    -79.98     2002 2002-01-01
## 12       quebec       quebec    46.85    -71.34     2002 2002-01-01
## 13   rapid city   rapid_city    44.06   -103.22     1995 1995-01-01
## 14    sarpsborg    sarpsborg    59.28     11.08     1995 1995-01-01
## 15       sydney       sydney   -33.85    150.93     2006 2006-01-01
##    year_max   date_max      onset before_onset
## 1      1999 1999-12-31 1999-02-25   1999-02-11
## 2      2015 2015-12-31 2015-07-12   2015-06-28
## 3      2013 2013-12-31 2013-07-09   2013-06-25
## 4      2014 2014-12-31 2014-06-06   2014-05-23
## 5      2015 2015-12-31 2015-05-04   2015-04-20
## 6      1989 1989-12-31 1989-01-01   1988-12-18
## 7      2000 2000-12-31 2000-04-17   2000-04-03
## 8      2002 2002-12-31 2002-07-18   2002-07-04
## 9      2001 2001-12-31 2001-06-26   2001-06-12
## 10     2006 2006-12-31 2006-06-01   2006-05-18
## 11     2012 2012-12-31 2012-08-26   2012-08-12
## 12     2012 2012-12-31 2012-07-18   2012-07-04
## 13     2005 2005-12-31 2005-05-26   2005-05-12
## 14     2005 2005-12-31 2005-05-12   2005-04-28
## 15     2016 2016-12-31 2016-04-25   2016-04-11

PLOT 1

Outbreak Distribution

This plot is divided by outbreaks in the northern and southern hemisphere. This allows us to see when the outbreaks generally occur in the year.

PLOT 2

10 years for all data

These plots allow for a quick glance into all the weather variables for each location.

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 27 rows containing missing values (geom_path).

PLOT 3

TMAX and TMIN

## Warning: Removed 1100 rows containing missing values (geom_path).

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 55 rows containing missing values (geom_path).

PLOT 4

Precipitation

I also made a loop to plot graphs and histograms of the data with lines indicating each day before the start of the outbreak for a total of 14 days.A plot of the percentiles is also included. The precentile data is saved for a plot of percentiles as shown later.

PLOT 5

TMAX

PLOT 6

TMIN

## Warning: Removed 550 rows containing missing values (geom_path).

## Warning: Removed 550 rows containing non-finite values (stat_bin).

## Warning: Removed 5 rows containing missing values (geom_path).

## Warning: Removed 1587 rows containing non-finite values (stat_bin).
## Warning: Removed 9 rows containing missing values (geom_vline).

## Warning: Removed 9 rows containing missing values (position_stack).

## Warning: Removed 1 rows containing missing values (geom_path).

## Warning: Removed 1595 rows containing non-finite values (stat_bin).
## Warning: Removed 5 rows containing missing values (geom_vline).

## Warning: Removed 5 rows containing missing values (position_stack).

## Warning: Removed 975 rows containing non-finite values (stat_bin).

## Warning: Removed 27 rows containing missing values (geom_path).

## Warning: Removed 27 rows containing non-finite values (stat_bin).

PERCENTILES

Table and Plots

Now I gathered a 2-week seasonal subset data for each weather variable and plotted it in a single table. I saved the table data for each outbreak and plotted them in a facetted plot.s

##     days_before_onset TMAX_year TMAX_seasonal TMIN_year TMIN_seasonal
## 1                   0 15.690799     54.814815 20.478800     56.296296
## 2                   1 13.700606     48.148148 15.806172     47.407407
## 3                   2 14.681281     52.592593 18.257860     51.851852
## 4                   3 11.479665     41.481481 13.614076     42.222222
## 5                   4 20.334583     64.444444 25.007211     66.666667
## 6                   5 15.690799     54.814815 23.622729     63.703704
## 7                   6 26.593597     74.074074 24.257283     65.925926
## 8                   7 18.402077     58.518519 18.257860     51.851852
## 9                   8  9.662532     35.555556  9.085665     33.333333
## 10                  9 16.094606     57.037037 11.681569     37.037037
## 11                 10 12.027690     43.703704 13.614076     42.222222
## 12                 11  6.864725     28.888889  3.345832     14.814815
## 13                 12  3.663109     13.333333  2.278627     10.370370
## 14                 13  6.201327     25.925926  5.018748     20.000000
## 15                 14  7.095472     29.629630  5.797519     22.222222
## 16                  0 93.104307     67.272727 90.589993     55.757576
## 17                  1 90.540204     61.818182 87.229276     44.848485
## 18                  2 84.266866     37.575758 92.805576     67.272727
## 19                  3 77.346278     19.393939 88.150361     47.878788
## 20                  4 93.552402     69.090909 99.004232     94.545455
## 21                  5 92.108539     64.848485 97.684839     89.090909
## 22                  6 83.619617     34.545455 90.589993     55.757576
## 23                  7 80.383371     29.090909 85.262634     36.969697
## 24                  8 72.417227     10.909091 86.706497     41.212121
## 25                  9 80.084640     27.878788 78.790142     20.000000
## 26                 10 77.918845     21.212121 85.362211     37.575758
## 27                 11 84.092606     36.363636 88.399303     49.090909
## 28                 12 78.317152     23.636364 80.657207     24.242424
## 29                 13 70.176749      6.060606 74.583022     12.727273
## 30                 14 65.098332      2.424242 72.840428      6.060606
## 31                  0 93.504231     72.727273 98.307616     96.969697
## 32                  1 77.277252     27.878788 87.605774     52.727273
## 33                  2 78.720757     32.121212 89.447486     60.000000
## 34                  3 82.901941     40.606061 97.137880     93.333333
## 35                  4 70.408163     13.939394 91.762071     69.696970
## 36                  5 71.577899     18.181818 97.735192     95.757576
## 37                  6 84.693878     46.666667 94.673967     84.848485
## 38                  7 79.193629     34.545455 92.956695     75.151515
## 39                  8 76.655052     26.060606 88.899950     56.969697
## 40                  9 72.025884     18.787879 78.795421     29.696970
## 41                 10 74.813340     21.818182 79.417621     31.515152
## 42                 11 83.026381     41.212121 88.402190     54.545455
## 43                 12 87.730214     54.545455 91.762071     69.696970
## 44                 13 91.513191     65.454545 91.762071     69.696970
## 45                 14 97.859632     89.696970 96.689895     90.909091
## 46                  0 72.573420     52.727273 61.796914     23.030303
## 47                  1 61.896466     21.212121 65.505226     36.363636
## 48                  2 73.693380     56.969697 78.546541     60.606061
## 49                  3 89.646590     80.000000 96.042807     98.181818
## 50                  4 92.160279     86.060606 88.128422     81.212121
## 51                  5 86.286710     75.151515 74.763564     55.757576
## 52                  6 86.112494     73.939394 75.012444     56.969697
## 53                  7 72.000996     49.696970 67.073171     41.212121
## 54                  8 65.878547     36.969697 63.787954     29.090909
## 55                  9 84.146341     71.515152 82.030861     70.909091
## 56                 10 90.019910     80.606061 86.137382     80.000000
## 57                 11 86.485814     75.757576 74.539572     54.545455
## 58                 12 76.704828     60.606061 65.281234     35.151515
## 59                 13 62.792434     24.242424 61.697362     21.818182
## 60                 14 64.211050     29.696970 62.667994     24.242424
## 61                  0 80.358476     95.757576 77.346278     96.969697
## 62                  1 72.641275     90.909091 61.463779     76.969697
## 63                  2 67.064974     80.000000 54.742345     60.606061
## 64                  3 52.128454     47.878788 49.639034     46.060606
## 65                  4 57.181977     64.848485 52.725915     54.545455
## 66                  5 54.543191     56.969697 43.241225     30.303030
## 67                  6 42.618870     26.060606 34.229525     12.121212
## 68                  7 44.983819     32.121212 41.224795     25.454545
## 69                  8 42.768235     27.272727 31.715210      7.272727
## 70                  9 42.544187     24.848485 31.167538      6.060606
## 71                 10 28.055763      3.030303 21.483694      1.212121
## 72                 11 24.794623      1.212121 31.814787      8.484848
## 73                 12 32.636296      6.666667 46.502365     40.606061
## 74                 13 51.058003     46.666667 52.003983     52.727273
## 75                 14 58.675629     69.090909 57.729649     68.484848
## 76                  0       NaN           NaN       NaN           NaN
## 77                  1 19.630872     18.765091       NaN           NaN
## 78                  2       NaN           NaN       NaN           NaN
## 79                  3       NaN           NaN 43.150967     42.361405
## 80                  4       NaN           NaN       NaN           NaN
## 81                  5       NaN           NaN       NaN           NaN
## 82                  6       NaN           NaN 65.981078     65.213711
## 83                  7 34.697987     33.425319 43.150967     42.361405
## 84                  8 42.248322     40.772680       NaN           NaN
## 85                  9 42.248322     40.772680       NaN           NaN
## 86                 10 34.697987     33.425319 64.253394     63.478629
## 87                 11 34.697987     33.425319 44.055944     43.250106
## 88                 12       NaN           NaN       NaN           NaN
## 89                 13 35.436242     34.046223       NaN           NaN
## 90                 14       NaN           NaN 22.459893     21.836648
## 91                  0 54.131409     46.060606 53.111000     44.848485
## 92                  1 72.722748     67.878788 84.544550     87.272727
## 93                  2 71.304131     66.666667 56.943753     48.484848
## 94                  3 68.292683     65.454545 94.524639     95.757576
## 95                  4 79.019413     78.787879 90.219014     92.727273
## 96                  5 85.689398     89.696970 72.772524     72.121212
## 97                  6 85.988054     90.303030 79.790941     81.818182
## 98                  7 82.055749     81.818182 74.489796     74.545455
## 99                  8 87.431558     92.121212 66.177203     63.030303
## 100                 9 82.503733     82.424242 31.408661     13.939394
## 101                10 61.448482     58.181818 41.961175     28.484848
## 102                11 58.536585     52.727273 36.635142     22.424242
## 103                12 53.583873     44.848485 76.754604     77.575758
## 104                13 72.175212     67.272727 52.040816     43.636364
## 105                14 74.066700     70.303030 70.930811     69.696970
## 106                 0 76.093071     16.149068       NaN           NaN
## 107                 1 86.934288     48.447205 82.872472     27.586207
## 108                 2 92.815137     64.596273       NaN           NaN
## 109                 3 83.175658     34.161491       NaN           NaN
## 110                 4 88.391716     52.173913 97.111019     87.068966
## 111                 5 90.948606     57.763975 98.060256     93.965517
## 112                 6 87.241115     49.068323 85.266199     42.241379
## 113                 7 87.496804     49.689441 83.945522     35.344828
## 114                 8 97.519816     81.366460       NaN           NaN
## 115                 9 87.599080     50.310559 97.853900     91.379310
## 116                10 88.263871     51.552795 87.825010     51.724138
## 117                11 84.709793     41.614907 84.275691     38.793103
## 118                12 83.584761     36.645963 91.209245     64.655172
## 119                13 77.371516     19.875776       NaN           NaN
## 120                14 77.090258     18.633540 95.542716     81.896552
## 121                 0 99.850672    100.000000 91.388751     95.757576
## 122                 1 99.004480     98.181818 84.395222     84.242424
## 123                 2 98.282728     95.757576 84.494774     85.454545
## 124                 3 99.825784     99.393939 78.496765     68.484848
## 125                 4 99.153808     98.787879 73.544052     50.909091
## 126                 5 93.852663     90.909091 68.367347     30.303030
## 127                 6 79.442509     60.606061 63.862618     13.939394
## 128                 7 63.364858     16.969697 72.847188     47.878788
## 129                 8 62.792434     15.151515 84.046789     83.030303
## 130                 9 75.684420     49.090909 67.098059     26.060606
## 131                10 86.361374     79.393939 90.467894     95.151515
## 132                11 96.416127     92.727273 78.496765     68.484848
## 133                12 94.848183     92.121212 79.890493     73.333333
## 134                13 87.680438     81.818182 69.362867     33.939394
## 135                14 63.937282     17.575758 65.355898     18.787879
## 136                 0 60.129418     30.303030 39.347934      8.484848
## 137                 1 46.565455     13.333333 51.294176     25.454545
## 138                 2 45.644599     11.515152 62.020906     51.515152
## 139                 3 63.937282     36.363636 78.571429     85.454545
## 140                 4 88.626182     86.666667 80.537581     87.272727
## 141                 5 92.458935     93.939394 69.437531     67.272727
## 142                 6 75.385764     58.787879 74.912892     79.393939
## 143                 7 72.598308     51.515152 37.406670      6.666667
## 144                 8 56.470881     24.848485 28.297661      1.212121
## 145                 9 42.508711      6.060606 42.483823     11.515152
## 146                10 63.265306     35.151515 66.923843     62.424242
## 147                11 85.813838     81.212121 72.125436     75.151515
## 148                12 65.778995     40.000000 62.344450     52.727273
## 149                13 63.066202     34.545455 54.952713     30.303030
## 150                14 65.778995     40.000000 82.205077     89.696970
## 151                 0 92.906919     83.636364 85.365854     53.333333
## 152                 1 92.906919     83.636364 82.478845     44.848485
## 153                 2 91.886511     78.181818 78.596317     32.121212
## 154                 3 84.420110     50.909091 74.589348     18.787879
## 155                 4 78.919861     30.909091 71.353907     11.515152
## 156                 5 75.958188     24.242424 72.448980     15.151515
## 157                 6 78.944749     31.515152 76.679940     24.848485
## 158                 7 75.236436     23.636364 76.779492     26.060606
## 159                 8 72.672972     16.969697 78.496765     31.515152
## 160                 9 79.417621     33.333333 89.472374     67.272727
## 161                10 87.605774     61.818182 83.349925     47.272727
## 162                11 80.711797     36.969697 87.580886     63.030303
## 163                12 85.788950     55.757576 87.282230     61.818182
## 164                13 79.766053     33.939394 83.822797     49.090909
## 165                14 67.894475      9.696970 83.051269     46.060606
## 166                 0 83.897461     32.121212 87.755102     38.787879
## 167                 1 73.842708     10.909091 97.336984     84.242424
## 168                 2 91.314087     60.606061 99.128920     93.333333
## 169                 3 99.253360     96.363636 93.529119     66.666667
## 170                 4 97.187656     84.848485 97.212544     83.636364
## 171                 5 99.104032     95.151515 95.843703     77.575758
## 172                 6 97.038328     83.636364 86.436038     34.545455
## 173                 7 91.015431     58.787879 73.544052      2.424242
## 174                 8 86.460926     41.212121 80.338477     16.969697
## 175                 9 85.042310     38.181818 82.852165     23.636364
## 176                10 79.168741     19.393939 89.024390     43.636364
## 177                11 96.042807     80.000000 98.158288     87.878788
## 178                12 93.056247     67.878788 93.728223     67.878788
## 179                13 90.841215     56.969697 94.300647     70.909091
## 180                14 81.981085     27.878788 93.529119     66.666667
## 181                 0 52.065704     30.909091 61.523146     43.030303
## 182                 1 52.090592     31.515152 63.339970     49.090909
## 183                 2 64.410154     61.212121 71.378795     77.575758
## 184                 3 73.668492     86.060606 76.356396     90.909091
## 185                 4 76.331508     92.121212 70.109507     75.757576
## 186                 5 79.965157     95.151515 81.159781     96.969697
## 187                 6 86.162270     98.181818 82.105525     97.575758
## 188                 7 84.619214     97.575758 73.444500     86.060606
## 189                 8 70.681931     79.393939 73.295172     85.454545
## 190                 9 70.532603     77.575758 72.672972     84.242424
## 191                10 75.136884     88.484848 72.025884     80.606061
## 192                11 66.898955     68.484848 45.943255      8.484848
## 193                12 45.196615     20.000000 44.997511      7.272727
## 194                13 39.696366      9.696970 42.259831      5.454545
## 195                14 26.903932      1.818182 38.352414      3.030303
## 196                 0 73.184358     72.500000 40.354913     14.166667
## 197                 1 64.344397     65.833333 46.171541     25.833333
## 198                 2 50.607953     31.666667 46.565889     27.500000
## 199                 3 50.607953     31.666667 44.134078     21.666667
## 200                 4 49.030562     26.666667 44.134078     21.666667
## 201                 5 52.152481     37.500000 48.209004     35.833333
## 202                 6 58.724942     52.500000 46.335853     26.666667
## 203                 7 52.152481     37.500000 34.538285      6.666667
## 204                 8 52.875452     38.333333 65.856063     87.500000
## 205                 9 58.724942     52.500000 63.851462     83.333333
## 206                10 50.607953     31.666667 59.710812     75.000000
## 207                11 62.668419     60.000000 59.710812     75.000000
## 208                12 47.321722     20.833333 60.893855     76.666667
## 209                13 57.837660     50.000000 52.875452     50.833333
## 210                14 64.344397     65.833333 42.885311     18.333333
## 211                 0 52.791109     53.333333 41.414141     21.818182
## 212                 1 46.956302     40.000000 59.772727     72.727273
## 213                 2 32.306138     17.575758 63.358586     81.212121
## 214                 3 69.133620     85.454545 59.545455     71.515152
## 215                 4 77.721647     92.121212 62.095960     77.575758
## 216                 5 70.093458     86.666667 59.848485     73.333333
## 217                 6 58.651175     65.454545 65.984848     85.454545
## 218                 7 54.079313     55.151515 56.363636     61.818182
## 219                 8 50.543066     48.484848 80.429293     96.969697
## 220                 9 81.156858     94.545455 55.075758     55.757576
## 221                10 76.483961     90.303030 58.813131     69.696970
## 222                11 53.447840     53.939394 54.898990     55.151515
## 223                12 57.438747     62.424242 56.565657     62.424242
## 224                13 49.078050     44.848485 74.015152     94.545455
## 225                14 70.346047     87.272727 43.787879     27.272727
##     PRCP_year PRCP_seasonal outbreak
## 1    53.33333      67.87879        1
## 2    40.00000      39.39394        1
## 3    17.57576      60.60606        1
## 4    85.45455      92.12121        1
## 5    92.12121      90.90909        1
## 6    86.66667      43.03030        1
## 7    65.45455      56.96970        1
## 8    55.15152      80.60606        1
## 9    48.48485      52.72727        1
## 10   94.54545      69.69697        1
## 11   90.30303      83.63636        1
## 12   53.93939      64.84848        1
## 13   62.42424      18.78788        1
## 14   44.84848      15.15152        1
## 15   87.27273      21.81818        1
## 16   53.33333      30.90909        2
## 17   40.00000      30.90909        2
## 18   17.57576      86.66667        2
## 19   85.45455      90.90909        2
## 20   92.12121      63.03030        2
## 21   86.66667      44.84848        2
## 22   65.45455      30.90909        2
## 23   55.15152      58.78788        2
## 24   48.48485      35.15152        2
## 25   94.54545      38.78788        2
## 26   90.30303      39.39394        2
## 27   53.93939      93.33333        2
## 28   62.42424      38.78788        2
## 29   44.84848      54.54545        2
## 30   87.27273      97.57576        2
## 31   53.33333      90.30303        3
## 32   40.00000      95.75758        3
## 33   17.57576      77.57576        3
## 34   85.45455      73.93939        3
## 35   92.12121      94.54545        3
## 36   86.66667      63.03030        3
## 37   65.45455      37.57576        3
## 38   55.15152      49.09091        3
## 39   48.48485      79.39394        3
## 40   94.54545      93.33333        3
## 41   90.30303      87.87879        3
## 42   53.93939      62.42424        3
## 43   62.42424      84.84848        3
## 44   44.84848      92.12121        3
## 45   87.27273      60.00000        3
## 46   53.33333      36.96970        4
## 47   40.00000      51.51515        4
## 48   17.57576      50.90909        4
## 49   85.45455      69.69697        4
## 50   92.12121      57.57576        4
## 51   86.66667      36.96970        4
## 52   65.45455      36.96970        4
## 53   55.15152      36.96970        4
## 54   48.48485      76.96970        4
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## Warning: Removed 8 rows containing missing values (position_stack).

## Warning: Removed 8 rows containing missing values (position_stack).

## Warning: Removed 14 rows containing missing values (position_stack).

## Warning: Removed 14 rows containing missing values (position_stack).

## Warning: Removed 5 rows containing missing values (position_stack).